# 전력 공급신뢰도 평가를 위한 교육용 소프트웨어 개발

• Accepted : 2015.06.11
• Published : 2015.07.30
• 30 12

#### Abstract

This paper is on the development of computer software which can be utilized as a power system analysis tool for reliability assessment education. The input data of the developed software are so simple that even a non-expert easily understand how to use it. The software provides not only reliability indices but also their distributions, moreover, it provides the factors those effect the indices, which made the software even more useful for educational purpose. The developed software utilized Monte-carlo simulation based on the state duration sampling, therefore it can manage various probability distributions such as exponential, Weibull, gamma and lognormal distribution. Within the software, the parameters of the distribution can be decided automatically from its mean and variance, that is another advantage as an educational software.

#### Keywords

Computer Software;Power System Education;Reliability Assessment

#### References

1. R. Billinton and G. Bai, "Generating Capacity Adequacy Associated With Wind Energy", IEEE Trans. on Energy Conversion, Vol.19, No.3, pp.641-646, September 2004. https://doi.org/10.1109/TEC.2004.827718
2. Z. Qin and W. Li, "Generation System Reliability Evaluation Incorporating Correlations of Wind Speeds With Different Distributions", IEEE Trans. on Power Systems, Vol.28, No.1, pp.551-558, February 2013. https://doi.org/10.1109/TPWRS.2012.2205410
3. Yi Ding, C. Singh, et al., "Short-Term and Midium-Term Reliability Evaluation for Power Systems with High Penetration of Wind Power", IEEE Trans. on Sustainablr Energy, Vol.5, No.3, pp.896-906, July 2014. https://doi.org/10.1109/TSTE.2014.2313017
4. R. Billinton and W. Li, Reliability Assessment of Electric Power Systems Using Monte Carlo Methods, Plenum Press, 1994.
5. R. Brown, Electric Power Distribution Reliability (2nd edition), CRC Press, 2009.
6. Math H. J. Bollen, Understanding Power Quality Problems, pp89-98, IEEE Press, 2009.
7. George F. Luger and William A. Stubblefield, Artificial Intelligence, pp86-100, Benjamin Cummings, 1993.
8. A. Leon-Garcia, Probability and Random Processes for Electrical Engineering (2nd edition), pp.99-119, Addison Wesley, 1994.
9. Gwang Won Kim, "A Study on the Substation Reliability Assessment Using Weibull Distribution", Trans. of the KIEE, Vol.51, No.1, pp.7-14, January 2002.